| Name: | Sarvesh Kumar Yadav |
| Affiliation: | Raman Research Institute, Bangalore |
| Conference ID : | ASI2024_142 |
| Title : | A machine learning based method to detect primordial gravitational wave signature |
| Authors : | Sarvesh Kumar Yadav, Rajib Saha, Tarun Souradeep |
| Authors Affiliation: | 1) Sarvesh Kumar Yadav (Raman Research Institute, Bangalore - 560080, India)
2) Rajib Saha (IISER Bhopal, Bhopal - 462066, India)
3) Tarun Souradeep (Raman Research Institute, Bangalore - 560080, India, IUCAA, Pune - 411 007, India )
|
| Mode of Presentation: | Poster |
| Abstract Category : | Galaxies and Cosmology |
| Abstract : | Observations of the Cosmic Microwave Background (CMB) radiation have made significant contributions to our understanding of cosmology. While temperature observations of the CMB have greatly advanced our knowledge, the next frontier lies in detecting the elusive B-modes and obtaining precise reconstructions of the polarized CMB signal in general. In anticipation of proposed and upcoming CMB polarization missions, this study introduces a novel method to detect the primordial B-modes. We have developed a Bayesian Neural Network (BNN)-based approach to enhance the performance of the Internal Linear Combination (ILC) technique. Our method is applied separately to the frequency channels of both the LiteBird and ECHO (also known as CMB-Bharat) missions and its performance is rigorously assessed for both missions. Our findings demonstrate the method's efficiency in achieving precise reconstructions of CMB B-mode angular power spectrum, with errors constrained primarily by cosmic variance. |
